This conference will provide a platform to showcase recent advances in applied and computational mathematics. Its primary objective is to offer a dynamic and interdisciplinary forum for researchers, academicians, and industry professionals to exchange ideas, present novel findings, and share insights into emerging research and real-world applications.The event aims to foster cross-disciplinary collaboration, promote innovative methodologies, and emphasize the role of mathematical tools and computational techniques in addressing complex challenges across science, engineering, and technology. By bringing together leading experts and early-career researchers from around the globe, the conference seeks to advance both theoretical and applied mathematical research. RDACM-2026 will feature public lectures, keynote addresses, minisymposia, oral presentations, and poster sessions, all aligned with the core themes of the event. Topics to be covered includes Differential Equations, Control Theory and Dynamical Systems, Numerical Analysis, Scientific Computing, Mathematical Modeling, Fluid Mechanics, Mathematical Biology, Artificial Intelligence & Machine Learning, Image Processing, Optimization & Operations Research, Operator Theory & Approximation Theory, Probability & Statistics, Fractional Calculus and Its Applications, Other emerging areas in applied and computational mathematics.

The Department of Mathematics, IIST is offereing Ph.D program and an M.Tech program in Machine Learning and Computing. Apart from these, the Department is offering courses for the UG and PG programs offered by other departments. Presently, there are eleven faculty members in the department, working in the research areas: Mathematical Control Theory; Suspension Rheology; Partial Differential Equations; Differential Geometry and its Applications; Applied Probability and Stochastic Processes; Numerical Solutions to Fluid Dynamics; Computational Partial Differential Equations; Numerical Solutions to Partial Differential Equations; Commutative Algebra; Machine Learning Algorithms and Applications: Control and Inverse Problems.